{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model: Decision Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import _pickle as pickle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import precision_score, recall_score, accuracy_score, f1_score, confusion_matrix, classification_report\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "# Importing the model\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading in Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('../top10_corr_features.xlsx')\n",
    "df = df.drop(df.columns[0], axis = 1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "\n",
    "features_df = df.drop([\"Decision\"], 1)\n",
    "\n",
    "scaled_df = pd.DataFrame(scaler.fit_transform(features_df), \n",
    "                               index=features_df.index, \n",
    "                               columns=features_df.columns)\n",
    "\n",
    "df = scaled_df.join(df.Decision)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop([\"Decision\"], 1)\n",
    "y = df.Decision\n",
    "\n",
    "# Train, test, split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Helper Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Function for plotting confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(y_true, y_pred, labels=[\"Sell\", \"Buy\", \"Hold\"], \n",
    "                          normalize=False, title=None, cmap=plt.cm.coolwarm):\n",
    "\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "    fig, ax = plt.subplots(figsize=(12,6))\n",
    "    im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    ax.figure.colorbar(im, ax=ax)\n",
    "    # We want to show all ticks...\n",
    "    ax.set(xticks=np.arange(cm.shape[1]),\n",
    "           yticks=np.arange(cm.shape[0]),\n",
    "           # ... and label them with the respective list entries\n",
    "           xticklabels=labels, yticklabels=labels,\n",
    "           title=title,\n",
    "           ylabel='ACTUAL',\n",
    "           xlabel='PREDICTED')\n",
    "    # Rotate the tick labels and set their alignment.\n",
    "    plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
    "             rotation_mode=\"anchor\")\n",
    "    # Loop over data dimensions and create text annotations.\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 1.5\n",
    "    for i in range(cm.shape[0]):\n",
    "        for j in range(cm.shape[1]):\n",
    "            ax.text(j, i, format(cm[i, j], fmt),\n",
    "                    ha=\"center\", va=\"center\",\n",
    "                    color=\"snow\" if cm[i, j] > thresh else \"orange\",\n",
    "                    size=26)\n",
    "    ax.grid(False)\n",
    "    fig.tight_layout()\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling\n",
    "The preferred evaluation metric used will be __Precision__ for each class.  They will be optimized using the __F1 Score-Macro-Average__ to balance the Precision and Recall.  This is done because we want to not only be correct when predicting but also make a decent amount of predictions for each class.  Classes such as 'Buy' and 'Sell' are more important than 'Hold'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting and Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "                       max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting and training\n",
    "clf = DecisionTreeClassifier()\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Printing out Evaluation Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        Sell       0.50      0.25      0.33         8\n",
      "         Buy       0.40      0.80      0.53         5\n",
      "        Hold       0.33      0.25      0.29         4\n",
      "\n",
      "    accuracy                           0.41        17\n",
      "   macro avg       0.41      0.43      0.38        17\n",
      "weighted avg       0.43      0.41      0.38        17\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classifier predictions\n",
    "pred = clf.predict(X_test)\n",
    "\n",
    "#Printing out results\n",
    "report = classification_report(y_test, pred, target_names=['Sell', 'Buy', 'Hold'])\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mVvOEkIrbPnWyNjOz2iegwN3gTtZmZlYXfJ+1mZmZDRi3rM3MrC4UeTa4k7WZmdW+ZI/MrKPoteJGbmZmVifcsjYzs7rgbnAzM7O8K/BscCdrMzOreZIKvZ91cX9mmJmZ1Qm3rM3MrD64G9zMzCzfPMHMzMwsz3yftZmZmQFIGiLpL5IekTRT0pkdlBks6TpJcyU9IGlKV/U6WZuZWX1oUN+PrrUC74iItwC7AgdL2quszInAKxGxHfB94NwuQ+/hf6qZmVkhSQ19ProSiZXp0+b0iLJihwNXpI9/CRyoLu4rc7I2M7Pat3E/6763rMdJmlFyTN/kraRGSQ8Di4A/RMQDZUUmAfMAIqINWA6MrRS+J5iZmZl13+KI2KNSgYjYAOwqaTRwg6SdI+LxkiIdtaLLW9+v42RtZmZ1QKjK91lHxDJJdwIHA6XJej4wGZgvqQkYBSytVJe7wc3MrD5IfT+6fAuNT1vUSGoBDgJmlRW7CTg+fXwUcHtEuGVtZmZWJROBKyQ1kjSIfxERt0g6C5gRETcBPwGukjSXpEV9TFeVOlmbmVntE1VZbjQiHgV26+D86SWP1wIf6Em9TtZmZlYHuteNnVdO1mZmVheqPcGsPxU3cjMzszrhlrWZmdU+UeiNPJyszcysDnR7be9ccrI2M7OaJ+jW2t55VdzIzczM6oRb1mZmVvs2buRRUE7WZmZWB1ToCWbFjdzMzKxOuGVtZmb1wSuYmZmZ5VyBVzBzsjYzs9onj1mbmZnZAHLL2szM6oNv3TIzM8u5AneDO1mbmVl9KPBs8OL+zDAzM6sTblmbmVntk3zrlpmZWe4VuBvcydrMzOpDgSeYFTdyMzOzOuGWtZmZ1T6PWefHsjWDufGxN2YdhvWjHbIOwPrdcZ97IesQrB89M29d1iF0X4HHrIv7M8PMzKxO1FTL2szMrFMFnmDmZG1mZnVAhe4Gd7I2M7PaJwo9way4kZuZmdUJt6zNzKzmBRDuBjczM8szeYKZmZlZ7hU4WRc3cjMzszrhlrWZmdUFj1mbmZnlmYo9Zl3cyM3MzHJG0mRJd0h6UtJMSZ/toMwBkpZLejg9Tu+qXreszcysPlSnG7wNOCUi/ippBPCQpD9ExBNl5e6OiMO6W6mTtZmZ1YcqrGAWEQuBhenjVyU9CUwCypN1j7gb3MzM6oAI9f0AxkmaUXJM7/QdpSnAbsADHVzeW9Ijkm6T9KauonfL2szMrPsWR8QeXRWSNBz4FfC5iFhRdvmvwDYRsVLSocCNwPaV6nPL2szMap9IZoP39ejOW0nNJIn6moj4dfn1iFgRESvTx7cCzZLGVarTLWszM6sLUYVbtyQJ+AnwZER8r5MyE4CXIiIk7UnScF5SqV4nazMzqwNV2896X+A44DFJD6fn/hPYGiAifggcBfybpDZgDXBMRESlSp2szczM+klE3EPS6V6pzIXAhT2p18nazMzqQjW6wQeKk7WZmdUHrw1uZmaWY14b3MzMzAaSW9ZmZlbzAm+RaWZmln/uBjczM7OB4pa1mZnVhah8+3OuOVmbmVkdkO+zNjMzy70CJ+viRm5mZlYn3LI2M7PaJ9+6ZWZmlmvhMWszM7MCKHDLurg/M8zMzOqEW9ZmZlYX3A1uZmaWa/KiKGZmZnlX5JZ1cSM3MzOrE25Zm5lZ7RP1Nxtc0uf6OxAzM7OBI4KGPh9Z6e07f75fozAzM7NO9bYbvLh9CWZmVneC+lxuNPo1CjMzswFW5NngnSZrSa/ScVIWMHTAIjIzMxsANXmfdUSMqGYg1okIxsQsJrT/hQntDzKh/UHGtT9KE+toYzDnD12ddYTWD4a+cTJvm3EDjS1DAHj4X7/M/Kt/k3FU1lP7T7qbI7a/iR02m8uw5pUsXjOOBxbuyc9nfZAXVk7KOjwrsB51g0saBhwBfDgi3jMwIVmpkfEc09bunHUYNsB2Of/01xK1FVFw2p7f5r3b3vq6s5OGL+Rftv8NB0/5Paffewb3L9wro/iMgu+61WXkkgZJOkLSL4CFwEHADwc8MtvEq5rEnMYjmN+wf9ahWD+adMxhjD9wb1Y9My/rUKyXPrrTNa8l6j8+93Y+ettPeM+vb+SLd53NgpUTGNq8hrP2PZPJI/wZZymkPh9Z6TRZS3qnpMuAZ4CjgKuApRFxQkTcXK0A690ajeU3g37ND1te4NKW57l58K+Y1/D2rMOyftI8eiQ7ffMLtLe18cSp38o6HOuFzYYs5bidrgbg3hf24vT7Tufvy7ZlWeto7nlhXz5z+/dZvX4IQ5vXMP3NP8442voVpHta9/HISqWW9e+AbYH9IuLYNEG3Vycs22i9RvD3psNZrQlZh2ID4B/OOYXBW4zl2Yt/xopHn8o6HOuFQ6b8nqHNawG45NGPU35n68JVE7n574cB8Lat7mazIUurHaLVgErJenfgz8D/SvqDpBOBxuqEZVb7NttnNyYf/37WLlzE7K9dlHU41kv7TroPgOdXbMXcZdt1WOaOeW8DoLGhnb0nPlC12KyEkjHrvh5Z6fSdI+JvEfHFiNgW+CqwGzBI0m2SplcrQLNapKYm3nzBGaihgSe++G3aXl2VdUjWSztsNgeAJ5b8Q6dlZi2dSlt78ud26pjZVYnLNlWr3eCviYh7I+JkYBJwHrD3gEZlVuO2/fzHGLHTdrx8+/0suP62rMOxXhrX8jJDm9cAsGDllp2WW98+iCVrxgKwzcjnqxKb1ZZKi6L8Y9mpABZHxO9IxrPNrBeGvmEy2582nQ2t63j8c2dnHY71wejBy197vKx1VMWyr7RuxhbDXmbkoBUDHZZ1osi3blW6z/q7HZwbI2kQcExEPNKTN5K0AXiMZPbFBuDkiLivJ3WY1YJdzv8vGluGMOfcS1g159msw7E+GNK09rXH6zYMqli2Nb3e0rRmQGOyzlWjG1vSZOBKYALJpOxLIuK/y8oI+G/gUGA1MC0i/lqp3kormHV4f5CkPYALgH/uyX8AsCYidk3reDfwDeBtPazDrNC2PPpQxh+0D6ufnc+cb/4o63Csj1SyInNXiUAdPLLqieotitIGnBIRf5U0AnhI0h8i4omSMocA26fHW4GL0//tVI8jj4gZwPCevq7MSOAVAEkHSLpl4wVJF0qaJulASTeUnH+npF/38X3NMtM0agQ7ffNUAB4/5Ru0r23NOCLrqzVtLa89HtxY+fMc1LgufY1XqqtlEbFwYys5Il4FniSZ71XqcODKSPwZGC1pYqV6e7zrlqQt6N2uWy2SHgaGABOBd3RR/nbgIknjI+Jl4ATgp714X7Nc2OHL/8aQCeN48ebbWXTrn7IOx/pB6Th16fh1R0YPXgbAinUjBzQm61y1Z3NLmkJyJ1X5/XqTgNLl7Oan5xZ2VlelCWYXsGlSHgPsA3y229H+v9Ju8L2BKyV1uuh1RISkq4BjJf2UZAb6RzuIczowHWCz8Vv3Iiyz6hg6JflxPeG97+CwNY9XLLvrpWez66XJ5LM/Tn0Xa55fMODxWc8tXjOe1etbGNq8honDO/07S3PDOsa1LAbguRX+O5WVfloudJykGSXPL4mIS8oLSRoO/Ar4XESUzyrsKJCKjeBKLesZZc8DWAJ8PiIWVaq0KxFxv6RxwHiS/v3S7vjSPqKfAjcDa4HrI6Ktg7ouAS4BmLzdHt5n28yqavYr27Pr5o/yprFPdlpm6pjZNDUkC0A+tXSHaoVmZSL6JVkvjog9KhWQ1EySqK+JiI6Gb+cDk0uebwVU/EVeKVm/PSKmVXpxb0nakWQ1tCXAc8BOkgaTJOoDgXsAImKBpAXAV4B3DkQsZtUy8wvfYvbXf9Dp9SETN2fPGy8G4KmzLuSl394BwNqFffptbAPs3hf2YdfNH2XrkfN446ineXr5Gzcp8/bJybDHhvYG7l9YcR6RFVw60/snwJMR8b1Oit0EnCzp5yQTy5ZHROddM1RO1m/uVaSd2zhmDUkXwPERsQGYl+7o9SgwB/hb2euuAcaXzaQzK5w1z71ApZt21i979f/LzlvotcIL4rZn38XHdrmclqa1fOLNP+aLd5/zuusThi3kfdsmex/9af7+vLJ2TBZhGiJ6Pqe6N/YFjgMeK8l5/wlsDRARPwRuJbltay7JrVsndFVppWQ9VNJudHKfQVf3hHVQvtN1xSPiVODUTi7vB1zak/eqNWPan2BwyZDH8JifPgombvjz68ouatiNDRpcxejM6tsra8dw5cxj+cRbfsx+W93HmfucyRUzj2Pp2jHsNPYJPrf7BQxtXsvq9S3pRh+WhY27bg34+0TcQxf350VEACf1pN5KyXoSycIonQ2EdzWbu88kPQSsAk4Z6PfKswPXnczk9k1nDzexjg+17vu6cz8e8ndWaEp1AjMzAK584iNsOXwB7932Vg7a5g4O2uaO111fvb6F0+89g3mvTu6kBquGLNf27qtKyXpuRAx4Qq4kInbP8v3NzLpHfPMvp3Lfgr05fLubmLrZHIY2r2LxmnH8ZeE/ce2so3lhZfmttmbd1+P7rK36rh9ye9YhWBWseX4Bt7R0ejejFcBd8/fnrvn7Zx2GdaLILetKo+3fkLRT+UlJb5I0fgBjMjMz62d93x4zr1tk/gvJfdDltiJZgNzMzMyqoFKy3iUiNpnVlG6R2d+3dZmZmQ2oCPX5yEqlMetK+70193cgZmZmA6Vat24NlEot69mSDi0/KekQ4OmBC8nMzKz/FXnMulLL+t+BWyR9EHgoPbcHyYYahw10YGZmZpbotGUdEbOBXYA/AVOAbYA7gY/Ru123zMzMMlOrLWsiohX4abrs6IeAM4BnSHYTMTMzK4hsJ4j1VaX9rHcAjiFJ0kuA6wBFxNurFJuZmVm/CKC9wBPMKrWsZwF3A++NiLkAkv69KlGZmZnZayrNBj8SeBG4Q9Klkg6ki51EzMzM8qrIY9aVJpjdEBFHAzuSTCz7d2ALSRdLeleV4jMzM+u7KPaiKF3uxB0RqyLimog4jGSp0YeB0wY8MjMzMwN6uOtWRCwFfpQeZmZmhVHkFcy8RaaZmdWBGr11y8zMrFbU8trgZmZmlgNuWZuZWV1wN7iZmVnOtWcdQB84WZuZWV0ocsvaY9ZmZmY555a1mZnVvKyXC+0rJ2szM6sLRe4Gd7I2M7O6UOSWtceszczMcs4tazMzq30B7ZF1EL3nZG1mZjXPy42amZnZgHLL2szM6oJng5uZmeVceMzazMwsz0S7x6zNzMxsoDhZm5lZzQuSMeu+Hl2RdJmkRZIe7+T6AZKWS3o4PU7vTvzuBjczs7pQpTHry4ELgSsrlLk7Ig7rSaVO1mZmVheqcZ91RNwlaUp/1+tucDMzs+4bJ2lGyTG9F3XsLekRSbdJelN3XuCWtZmZ1b7+W250cUTs0YfX/xXYJiJWSjoUuBHYvqsXuWVtZmY1r1oTzLqMI2JFRKxMH98KNEsa19XrnKzNzMyqRNIESUof70mSh5d09Tp3g5uZWV2oxmxwSdcCB5CMbc8HzgCak/ePHwJHAf8mqQ1YAxwT0XVkTtZmZlYXqrGCWUR8qIvrF5Lc2tUjTtZmZlYXirw2uMeszczMcs4tazMzq3lB/8zmzoqTtZmZ1b7+u886E07WZmZWFzxmbWZmZgPGLWszM6sL1djIY6A4WZuZWc0LPGZtZmaWe0Ues3aytlzb6anfZh2C9bMjH3tj1iFYP3r+yRFZh1AXnKzNzKwuuGVtZmaWYxHQXuBFUXzrlpmZWc65ZW1mZnXB3eBmZmY552RtZmaWc0W+z9pj1mZmZjnnlrWZmdW8AG+RaWZmlmvhMWszM7Pc85i1mZmZDRi3rM3MrOYlY9ZZR9F7TtZmZlYXipys3Q1uZmaWc25Zm5lZXSjyBDMnazMzq32+dcvMzCzfAmhvzzqK3vOYtZmZWc65ZW1mZnXB3eBmZmY552RtZmaWYxHFng3uMWszM7Occ8vazMzqQhS4H9zJ2szM6kKBc7WTtZmZ1QffZ21mZmZIukzSIkmPd3Jdks6XNFfSo5L+sTv1OlmbmVnNi+ifoxsuBw6ucP0QYPv0mA5c3J1K3Q1uZmZ1oRq3bkXEXZKmVChyOHBlJLPd/ixptKSJEbGwUr1uWZuZmXXfOEkzSqqL9U0AABE8SURBVI7pPXz9JGBeyfP56bmK3LI2M7O60E+zwRdHxB59eL06ONdlZE7WZmZWFyIfS5jNByaXPN8KWNDVi9wNbmZmNW/jcqN9PfrBTcBH01nhewHLuxqvBreszczM+o2ka4EDSMa25wNnAM0AEfFD4FbgUGAusBo4oTv1OlmbmVldqMYKZhHxoS6uB3BST+t1sjYzs7rQno8x615xsjYzs5oXFHttcE8wMzMzyzm3rM3MrPZ1f7nQXHKyNjOzOhC0FzhbuxvczMws59yyNjOzuhAF3s/aydrMzGpeMhu8uN3gTtZmZlb7AtoL3LL2mLWZmVnOuWVtZmZ1wd3gNnAiGBOzmND+Fya0P8iE9gcZ1/4oTayjjcGcP3R11hFaD6l9LS3L7mLo8rsZvPIxmlqfp6F9De2NI1g3dEdWjXk3r44/kmgYknWo1h3+jhZC0G+7ZmXCyTrnRsZzTFu7c9ZhWD/a5qG9aGhftcn5xrZXaFlxPy0r7mfki1fy4tQf0TZkSvUDtB7xd7QgIjf7WfeKk3WBvKpJvNjwT7TEErZqvzvrcKyXGtpX0a5BrB7zLlZtdhCtw3ahvWkUTa0LGLHo54xcdC2D1j7NxFknMH+XW4jGYVmHbN3k76gNFCfrnFujsfxm0K9Z2PhWVmsCAHuvO9N/CAps+RbHsmzSSWxoHve68+uaRrHkDWfSNnhLxs77Ds2t8xn50jUs33J6RpFad/g7WhwFHrL2bPC8W68R/L3p8Nf+CFjxLZlyxiaJutTyiSeyoWkzAIYuu6taYVkv+TtaHO3t0ecjK25Zm+WNmlg/ZBsaV75C4/pFWUdjVhMiotCzwd2yNsuhxvWLAWhvHJ5xJGaWB25Zm+XMoFUzaW6dD0Dr8F0zjsasdnhtcDPrN2Oe/xYAgVix+dEZR2NWO7xFZgckrSx7Pk3ShV285quS/qOD81MkPd7fMZrlzagFlzJ0xX0ArNj8w6wfOjXjiMwsD9yyNsuJlmV3M2bedwFobZnK0m1Oyzgis9pS5AlmmSRrSdsAlwHjgZeBEyLi+bIyu6dlVgP3VD1IsyoatOpxtpjzacQG2gZN5MWpl3q5UbN+FEGmt1711UDOBm+R9PDGAzir5NqFwJUR8WbgGuD8Dl7/U+AzEbF3pTeRNF3SDEkzVq14ud+CN6uW5jXPMHHWiTS0r2JD0xgW7vhTNgyemHVYZjUnou9HVgYyWa+JiF03HsDpJdf2Bn6WPr4K2K/0hZJGAaMj4k8lZToUEZdExB4RscewkeP7MXyzgdfYupAJs6bR2LaU9sbhLNzxMta3bJt1WGaWM3kZsy7/vaIOzpnVlIb1S5g4axrN6xbQ3jCEF3f4EeuGvSnrsMxqVpE38shqUZT7gGPSxx+hbEw6IpYByyXtV1LGrGao7VUmzjqRQWufJtTMS9tfwNqRe2YdllnNigja++HISlYt688Al0n6AukEsw7KnJCWWQ38rprBmQ0ktbcyYfYnGLx6JkEDi7b9NmtGH5B1WGY1r8gt6wFL1hExvOz55cDl6eNngXd08Jqvljx+CHhLyeWvlpevF2Pan2BwrHjt+fCYnz4KJm748+vKLmrYjQ0aXMXorEdiA5vP+Swtrz4IwNKtT2P16APQhk33t040EI0t1YvPesXfURtoeRmztgoOXHcyk9v/tMn5JtbxodZ9X3fux0P+zgpNqU5g1mNN6xYybNkfX3s+9vlzGPv8OZ2WXz9oEvN2u7MKkVlf+DtaDG5Zm5mZ5VlAgXO1k3URXD/k9qxDsH7SNngrnn7rnKzDsH7m76gNNCdrMzOreYG7wc3MzHIuvDa4mZlZrnltcDMzMwOQdLCkpyTNlbTJ1nnpdtEvl+yd8fHu1OuWtZmZ1YWB7gaX1AhcBLwTmA88KOmmiHiirOh1EXFyT+p2sjYzs5pXpQlmewJzI+JpAEk/Bw4HypN1j7kb3MzMal8kybqvBzBu47bM6TG95F0mAfNKns9Pz5U7UtKjkn4paXJ3wnfL2szMrPsWR8QenVxTB+fKm/M3A9dGRKukTwJX0MHy2+WcrM3MrA5UZdes+UBpS3krYMHroohYUvL0UuDc7lTsZG1mZnWhCmPWDwLbS3oD8ALJVtAfLi0gaWJELEyfvg94sjsVO1mbmVnNCwZ+NnhEtEk6mWRb50bgsoiYKeksYEZE3AR8RtL7gDZgKTCtO3U7WZuZmfWTiLgVuLXs3Oklj78EfKmn9TpZm5lZ7Sv4CmZO1mZmVheKvJGH77M2MzPLObeszcysDnjXLTMzs1yLgGhvzzqMXnOyNjOzulDkCWYeszYzM8s5t6zNzKwueMzazMwszyIKfeuWk7WZmdW8Ku1nPWA8Zm1mZpZzblmbmVldaA/fumVmZpZf4W5wMzMzG0BuWZuZWc0LPBvczMws93yftZmZWZ4FtBd4bXCPWZuZmeWcW9ZmZlYXPGZtZmaWY0EQvs/azMwsx3yftZmZmQ0kt6zNzKwuFLll7WRtZmZ1ILw2uJmZWZ6Fx6zNzMxsILllbWZmdSEKvIKZk7WZmdU+d4ObmZnZQHLL2szM6oBXMDMzM8u1ANoL3A3uZG1mZrUvij3BzGPWZmZmOeeWtZmZ1YHwbHAzM7O8i2jv89EVSQdLekrSXEmndXB9sKTr0usPSJrSndidrM3MrPal91n39ahEUiNwEXAIsBPwIUk7lRU7EXglIrYDvg+c253wnazNzMz6x57A3Ih4OiLWAT8HDi8rczhwRfr4l8CBktRVxR6zNjOzmhdENWaDTwLmlTyfD7y1szIR0SZpOTAWWFyp4ppK1vP//tDiU97f+FzWcVTBOLr4YGvFKVkHUD1185nWkXr5TLfJOoDuWLV89u/uveWAcf1Q1RBJM0qeXxIRl6SPO2ohl/edd6fMJmoqWUfE+KxjqAZJMyJij6zjsP7jz7T2+DPNl4g4uApvMx+YXPJ8K2BBJ2XmS2oCRgFLu6rYY9ZmZmb940Fge0lvkDQIOAa4qazMTcDx6eOjgNsjor5a1mZmZllJx6BPBn4HNAKXRcRMSWcBMyLiJuAnwFWS5pK0qI/pTt3qRkK3nJE0vWSMxGqAP9Pa48/U+pOTtZmZWc55zNrMzCznnKzNzMxyzsm6YLqz0o2ZZUfS6KxjsNrjZF0gkv4J+Kiklqxjsf6X3nNpBSZpEnCvpHdkHYvVFifrYhkGnAwcKWlI1sFY/5G0A3CxpMFZx2K9I0kR8QLwbeDbkvbKOiarHU7WBSBpF0nHRcSdJCtwfhz4oBN28ZUMawwC2knuzbSCSRP1xltrngWeAy6RtE92UVktcbIuhl2A90v6cETcBXwV+BhO2LVgZPq/TwFbAGdkGIv10sZEnS6IcTbJbku3Az+U9M9Zxma1wck6xza2uiLiZ8D1wHskHZu2sL9KkrCP8hh2MUnaCrhS0okRsZ5kiGOYpEJsjGAgaaqkQ0pObQ/8V0T8Avg88EPg+5L2yyRAqxme0JJTZd1qRMS1klYAx0kiIq6WdDpwPrAeuC6rWK3nJG1NslXe94D/kPRmkpZ1C7Aj8Fz5/wcsXyQ1A0cCk9KP6n9IPr9jSdZ7bpd0O/Ah4JuS3hkRazIM2QrMK5jlnKR/BbYG1gIXAfuRrCV7a0T8TNK+wPyIqIetQQtPUgPJLjvfJNnT9rskW+aNJpmPcBDwKnBURLyYVZzWPZImAB8FJpL8YH4SuBV4ICI+L+loYGfgwoh4KbtIreicrHNG0tCIWJ0+/gzwPuAs4DzgVxFxtqQPkPyBuCrtbrOcK28lp7f2fAB4GrghIuam598EfAL4SUQ8kkmwVlEHn+V4kiGpySRj1XOAX5FsjbgLyQ+vmVnEarXDY9Y5IulQ4BxJkyU1knz53w3sAbxIcjvIoIi4HvgRcG920VpPRERIeoukC9LntwPXkoxxHi3pjen5mSR74L4rs2CtU6WJWtJ7JR0MTI2Ic0lmgB8NbB0R+wHTgP2cqK0/OFnnhKTDgG8Ad0bEPJLbeLYC7iTp+j48ItYBH5P0voi4Jb2n03JK0raS/kXSEemp9cAYSd9P/+jfBfwW+CTwL5JGSxpG0iV+a0ZhWzdI+hRJj9d+wKWSvhwR3ya5beuTkg6MiNURsSTLOK12OFnnQDrudQrw8Yi4UdKQ9Nf75SRjYVdHxHpJ04DPAv6lnnPpIie/AfYFTpX0sYh4guS2nlEkwxoAjwB/A/4nIpZFxCrgELfG8kXSdpJGpT0km5MMYXw4Ir4C7EPyI3oa8GPgceCx7KK1WuTZ4PnQStLqWpveN32apLeRTDRaSrK4wiHArsCREfH37EK1rkjaCbgG+FJE3CzpWGCkpDelG9F/Czhb0v0krejPRcTjJV2s6zIM38pI2gw4CVgn6RsRsUjSEtLPKSJekfTvwL4Rcbmk8yNiQ5YxW+3xBLMcSO+n/jzJOOWbgP8F7gGeAI4AZgM3AA0R8XJWcVr3pPfU3hURDenzR4EXgC2Bv0XEtPT8e4AXIuLhrGK1zm388ZR+Pw8maUFvAM4EzgHeCewVEW2SPg3sRTLxs9233Fl/c8s6B9I/CD8C7iOZVPabiGgFkDQdeNRjX8UREfdIeo+kp0lme/8yIs6SNAh4TNJXIuLrEfHbjEO1yhqBNpJGzW2SRgKnAqsi4kuSRgB3pT/G3gp8xC1qGyhuWedYeovWacAH3fVdPJIOBH4HDIqI9vTcicDoiPhupsFZRZLGATOAPdNu7y1JVhF8BFgJvBIR35D0jyRzEJ6NiGeyi9hqnSeY5ZCkiZI+R7Kk6PFO1MUUEX8kuU9+NiSTlIAv4MlHuRcRi4FPA7dL2hm4CvhZRHyKZKb+5pLOBeZGxB1O1DbQ3A2eT8tIFlY4fONiGVZMEXGrpHZJq4FnSCaT/T7ruKxr6eTA9cCjwH9GxEXppbuBwcD+6f+aDTh3g5tVQdolPjIibsg6FusZSe8ELgDeGhHLS86/ttqg2UBzsjarIm/OUUzprZPnAXtHxNKs47H6425wsypyoi6mdDb4IOB/Je2RnPJnadXjlrWZWTdJGh4RK7OOw+qPk7WZmVnO+dYtMzOznHOyNjMzyzknazMzs5xzsjYzM8s537pl1glJG0iWBm0CniRZ+nV12flngOMiYpmkKWm5p0qq+V5EXCnpWZItTyHZIOLXwNciojV93S0RsXP6vnsC3wG2AIJkB7a/Af+avn6n9D02AP8DzAK+TbKz10YfBlan8cwChqTvf1FEXNHHfxozqzLPBjfrhKSVETE8fXwN8FBEfK/s/BXA7Ig4uzzpltX1LLBHRCyWNBy4BFgfEceXvk7SFsBfgGMi4v50e8Yjgbsj4qXyutLn09LnJ5e95+vikfRGkh8J/x0RP+2nfyYzqwJ3g5t1z93Adh2cvx+Y1JOK0vt0PwkcIWlM2eWTgCsi4v60bETELzcm6r6IiKdJ9k3/TF/rMrPqcrI264KkJuAQynbLktQIHAjcVHJ6W0kPlxz7d1RnRKwg6ULfvuzSzsBDvQjz6LL3bemk3F+BHXtRv5llyGPWZp1rkfRw+vhu4Cdl56eQJNY/lLzm7xGxazfrV79Embiug27wgX5PM6sSt6zNOrcmInZNj09HxLrS88A2wCCSrusekTSCJNnPLrs0E9i9DzF3ZTeSSWdmViBO1ma9lG6X+BngPyQ1d/d16QSzHwA3RsQrZZcvBI6X9NaS8sdKmtDXeNMJZ98h2e7RzArE3eBmfRARf5P0CHAMSVf5tiVd5wCXRcT56eM70tndDcANwNc6qO8lSccA35G0OdAO3EUyi7uSoyXtV/L8U8CCNJ6/8f+3bl3gmeBmxeNbt8zMzHLO3eBmZmY552RtZmaWc07WZmZmOedkbWZmlnNO1mZmZjnnZG1mZpZzTtZmZmY593+C7lI1Y5a5CQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_test, pred, title=\"Confusion Matrix\")\n",
    "np.set_printoptions(precision=1)\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tuning Model Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parameters to Tune\n",
    "params = {'criterion': ['gini', 'entropy'],\n",
    "          'max_depth': [None, 2, 3, 4, 5, 6],\n",
    "          'min_samples_split': [2, 5, 10],\n",
    "          'min_samples_leaf': [1,2,3,4,5,6]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 216 candidates, totalling 648 fits\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=2, score=(train=1.000, test=0.327), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=2, score=(train=1.000, test=0.336), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=2, score=(train=1.000, test=0.199), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, score=(train=0.890, test=0.233), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, score=(train=0.866, test=0.309), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, score=(train=0.918, test=0.192), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=10, score=(train=0.811, test=0.321), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=10, score=(train=0.705, test=0.416), total=   0.0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=10, score=(train=0.803, test=0.219), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=2, min_samples_split=2, score=(train=0.881, test=0.307), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=2, min_samples_split=2, score=(train=0.909, test=0.300), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=2, min_samples_split=2, score=(train=0.839, test=0.286), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=2, min_samples_split=5 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=2, min_samples_split=5, score=(train=0.850, test=0.329), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=2, min_samples_split=5 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=2, min_samples_split=5, score=(train=0.817, test=0.293), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=2, min_samples_split=5 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=2, min_samples_split=5, score=(train=0.820, test=0.222), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=2, min_samples_split=10, score=(train=0.761, test=0.236), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=2, min_samples_split=10, score=(train=0.672, test=0.419), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=2, min_samples_split=10, score=(train=0.803, test=0.219), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=3, min_samples_split=2 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=3, min_samples_split=2, score=(train=0.832, test=0.329), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=3, min_samples_split=2 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=3, min_samples_split=2, score=(train=0.765, test=0.302), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=3, min_samples_split=2 \n",
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
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      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
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     "output_type": "stream",
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
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     "output_type": "stream",
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  criterion=entropy, max_depth=2, min_samples_leaf=3, min_samples_split=5, score=(train=0.461, test=0.375), total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=2, min_samples_leaf=3, min_samples_split=10 \n",
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      "[CV] criterion=entropy, max_depth=2, min_samples_leaf=3, min_samples_split=10 \n",
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      "[CV] criterion=entropy, max_depth=2, min_samples_leaf=4, min_samples_split=2 \n",
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      "[CV] criterion=entropy, max_depth=2, min_samples_leaf=6, min_samples_split=10 \n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
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      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
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      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
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      "  'precision', 'predicted', average, warn_for)\n",
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
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      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
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      "[CV] criterion=entropy, max_depth=6, min_samples_leaf=4, min_samples_split=10 \n",
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      "[CV] criterion=entropy, max_depth=6, min_samples_leaf=4, min_samples_split=10 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  criterion=entropy, max_depth=6, min_samples_leaf=4, min_samples_split=10, score=(train=0.654, test=0.270), total=   0.0s\n",
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      "[CV] criterion=entropy, max_depth=6, min_samples_leaf=6, min_samples_split=5 \n",
      "[CV]  criterion=entropy, max_depth=6, min_samples_leaf=6, min_samples_split=5, score=(train=0.722, test=0.156), total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=6, min_samples_leaf=6, min_samples_split=5 \n",
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      "[CV]  criterion=entropy, max_depth=6, min_samples_leaf=6, min_samples_split=10, score=(train=0.722, test=0.156), total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=6, min_samples_leaf=6, min_samples_split=10 \n",
      "[CV]  criterion=entropy, max_depth=6, min_samples_leaf=6, min_samples_split=10, score=(train=0.461, test=0.268), total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=6, min_samples_leaf=6, min_samples_split=10 \n",
      "[CV]  criterion=entropy, max_depth=6, min_samples_leaf=6, min_samples_split=10, score=(train=0.465, test=0.330), total=   0.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "[Parallel(n_jobs=1)]: Done 648 out of 648 | elapsed:    7.7s finished\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:813: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score='raise-deprecating',\n",
       "             estimator=DecisionTreeClassifier(class_weight=None,\n",
       "                                              criterion='gini', max_depth=None,\n",
       "                                              max_features=None,\n",
       "                                              max_leaf_nodes=None,\n",
       "                                              min_impurity_decrease=0.0,\n",
       "                                              min_impurity_split=None,\n",
       "                                              min_samples_leaf=1,\n",
       "                                              min_samples_split=2,\n",
       "                                              min_weight_fraction_leaf=0.0,\n",
       "                                              presort=False, random_state=None,\n",
       "                                              splitter='best'),\n",
       "             iid='warn', n_jobs=None,\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': [None, 2, 3, 4, 5, 6],\n",
       "                         'min_samples_leaf': [1, 2, 3, 4, 5, 6],\n",
       "                         'min_samples_split': [2, 5, 10]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "             scoring='f1_macro', verbose=5)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search = GridSearchCV(clf, params, cv=3, return_train_score=True, verbose=5, scoring='f1_macro')\n",
    "\n",
    "search.fit(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tuned Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Training Score: 0.6992864958741142\n",
      "Mean Testing Score: 0.7085044283413847\n",
      "\n",
      "Best Parameter Found:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'criterion': 'entropy',\n",
       " 'max_depth': 5,\n",
       " 'min_samples_leaf': 4,\n",
       " 'min_samples_split': 2}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Mean Training Score:\", np.mean(search.cv_results_['mean_train_score']))\n",
    "print(\"Mean Testing Score:\", search.score(X, y))\n",
    "print(\"\\nBest Parameter Found:\")\n",
    "search.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model with the Best Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=5,\n",
       "                       max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=4, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search_clf = search.best_estimator_\n",
    "\n",
    "search_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Results from Optimum Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        Sell       0.20      0.12      0.15         8\n",
      "         Buy       0.30      0.60      0.40         5\n",
      "        Hold       0.50      0.25      0.33         4\n",
      "\n",
      "    accuracy                           0.29        17\n",
      "   macro avg       0.33      0.33      0.30        17\n",
      "weighted avg       0.30      0.29      0.27        17\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classifier predictions\n",
    "s_pred = search_clf.predict(X_test)\n",
    "\n",
    "#Printing out results\n",
    "report = classification_report(y_test, s_pred, target_names=['Sell', 'Buy', 'Hold'])\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix for Optimum Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_test, s_pred, title=\"Confusion Matrix\")\n",
    "np.set_printoptions(precision=1)\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
